Establishing a Relation between Preisach and Jiles–Atherton Models
Bibliographic record
Abstract
Hysteresis models can incorporate the effects of operating conditions (frequency, stress, and temperature) on iron losses incurred in ferromagnetic materials. Among such models, the Jiles-Atherton (JA) and Preisach models are the most popular and various modifications of these have been proposed in the literature to model the effect of frequency, stress, and temperature on iron losses. Both of these representations produce accurate results compared with the curve fitting models and can be directly implemented in finite element simulations. Unfortunately, it is very difficult to incorporate all these effects into a single iron loss model that is computationally efficient and can predict iron losses with reasonable accuracy for a given range of these parameters. In this paper, an effort has been made to establish a relationship between JA and Preisach models and an interpolation-based approach is presented to predict iron losses that utilizes the Preisach model on top of the JA model and incorporates all of the above factors. It is shown that iron loss can be predicted accurately for any value of frequency, stress, and temperature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".